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The priority of the automotive industry is to reduce the energy consumption and the emissions of the future passenger cars and to deliver an efficient mobility service for the customers.
The improvement of the efficiency of vehicle energy systems promotes an active search to find innovative solutions during the design process. Engineers can use computer-aided processes to find automatically the best design solutions. This kind of approach named “multi-objective optimization” is based on genetic algorithms. The idea is to obtain simultaneously a population of possible design solutions corresponding to the most efficient energy system definition for a vehicle. These solutions will be optimal from technical, economic and environmental point of view. The “genetic intelligence” is tested for the holistic design of the environomic vehicle powertrain solutions. The environomic methodology for design is applied on D-class hybrid electric vehicles, in order to explore the techno-economic and environmental trade-off for different hybridization level of the vehicles powertrains. The method gives also an overview of the evolution of environmental categories indicators as a function of the cost of the vehicles.
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